WPNets and PWNets: From the Perspective of Channel Fusion
Bibliographic record
Abstract
The performance and parameters of neural networks have a positive correlation, and there are a lot of parameter redundancies in the existing neural network architectures. By exploring the channels relationship of the whole and part of the neural network, the architectures of the convolution network with the tradeoff between the parameters and the performance are obtained. Two network architectures are implemented by dividing the convolution kernels of one layer into multiple groups, thus ensuring that the network has more connections and fewer parameters. In these two network architectures, the information of one network flows from the whole to the part, which is called whole-to-part connected networks (WPNets), and the information of the other network flows from the part to the whole, which is called part-to-whole connected networks (PWNets). WPNets use the whole channel information to enhance partial channel information, and the PWNets use partial channel information to generate or enhance the whole channel information. We evaluate the proposed architectures on three competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet), and our models obtain comparable results even with far fewer parameters compared to many state of the arts. Our network architecture code is available at github.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".